task_path
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⌀ | metric_value
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⌀ |
|---|---|---|---|---|---|
Stereo Depth Estimation
|
KITTI2015
|
3D-MobileStereoNet
|
https://arxiv.org/abs/2108.09770v1
|
three pixel error
|
1.69
|
Stereo Depth Estimation
|
KITTI2015
|
CDN-GANet Deep
|
https://arxiv.org/abs/2007.03085v2
|
three pixel error
|
1.92
|
Stereo Depth Estimation
|
KITTI2015
|
HITNET
|
https://arxiv.org/abs/2007.12140v5
|
three pixel error
|
2.43
|
Stereo Depth Estimation
|
KITTI2015
|
2D-MobileStereoNet
|
https://arxiv.org/abs/2108.09770v1
|
three pixel error
|
2.67
|
Stereo Depth Estimation
|
KITTI2015
|
TriStereoNet
|
https://arxiv.org/abs/2111.12502v2
|
D1-all All
|
2.35
|
Stereo Depth Estimation
|
KITTI2015
|
TriStereoNet
|
https://arxiv.org/abs/2111.12502v2
|
D1-all Noc
|
2.09
|
Stereo Depth Estimation
|
KITTI2015
|
ChiTransformer
|
http://openaccess.thecvf.com//content/CVPR2022/html/Su_Chitransformer_Towards_Reliable_Stereo_From_Cues_CVPR_2022_paper.html
|
D1-all All
|
2.60 (self-sup.)
|
Stereo Depth Estimation
|
KITTI2015
|
ChiTransformer
|
http://openaccess.thecvf.com//content/CVPR2022/html/Su_Chitransformer_Towards_Reliable_Stereo_From_Cues_CVPR_2022_paper.html
|
D1-all Noc
|
2.38 (self-sup.)
|
Stereo Depth Estimation > Omnnidirectional Stereo Depth Estimation
|
Helvipad
|
DFI-OmniStereo
|
https://arxiv.org/abs/2503.23502v1
|
Depth-MAE
|
1.463
|
Stereo Depth Estimation > Omnnidirectional Stereo Depth Estimation
|
Helvipad
|
DFI-OmniStereo
|
https://arxiv.org/abs/2503.23502v1
|
Depth-RMSE
|
3.767
|
Stereo Depth Estimation > Omnnidirectional Stereo Depth Estimation
|
Helvipad
|
DFI-OmniStereo
|
https://arxiv.org/abs/2503.23502v1
|
Depth-MARE
|
0.108
|
Stereo Depth Estimation > Omnnidirectional Stereo Depth Estimation
|
Helvipad
|
DFI-OmniStereo
|
https://arxiv.org/abs/2503.23502v1
|
Depth-LRCE
|
0.397
|
Stereo Depth Estimation > Omnnidirectional Stereo Depth Estimation
|
Helvipad
|
DFI-OmniStereo
|
https://arxiv.org/abs/2503.23502v1
|
Disp-MAE
|
0.158
|
Stereo Depth Estimation > Omnnidirectional Stereo Depth Estimation
|
Helvipad
|
DFI-OmniStereo
|
https://arxiv.org/abs/2503.23502v1
|
Disp-RMSE
|
0.338
|
Stereo Depth Estimation > Omnnidirectional Stereo Depth Estimation
|
Helvipad
|
DFI-OmniStereo
|
https://arxiv.org/abs/2503.23502v1
|
Disp-MARE
|
0.120
|
Stereo Depth Estimation > Omnnidirectional Stereo Depth Estimation
|
Helvipad
|
DFI-OmniStereo
|
https://arxiv.org/abs/2503.23502v1
|
Disp-LRCE
|
0.058
|
Stereo Depth Estimation > Omnnidirectional Stereo Depth Estimation
|
Helvipad
|
360-IGEV-Stereo
|
https://arxiv.org/abs/2411.18335v2
|
Depth-MAE
|
1.720
|
Stereo Depth Estimation > Omnnidirectional Stereo Depth Estimation
|
Helvipad
|
360-IGEV-Stereo
|
https://arxiv.org/abs/2411.18335v2
|
Depth-RMSE
|
4.297
|
Stereo Depth Estimation > Omnnidirectional Stereo Depth Estimation
|
Helvipad
|
360-IGEV-Stereo
|
https://arxiv.org/abs/2411.18335v2
|
Depth-MARE
|
0.130
|
Stereo Depth Estimation > Omnnidirectional Stereo Depth Estimation
|
Helvipad
|
360-IGEV-Stereo
|
https://arxiv.org/abs/2411.18335v2
|
Depth-LRCE
|
0.388
|
Stereo Depth Estimation > Omnnidirectional Stereo Depth Estimation
|
Helvipad
|
360-IGEV-Stereo
|
https://arxiv.org/abs/2411.18335v2
|
Disp-MAE
|
0.188
|
Stereo Depth Estimation > Omnnidirectional Stereo Depth Estimation
|
Helvipad
|
360-IGEV-Stereo
|
https://arxiv.org/abs/2411.18335v2
|
Disp-RMSE
|
0.404
|
Stereo Depth Estimation > Omnnidirectional Stereo Depth Estimation
|
Helvipad
|
360-IGEV-Stereo
|
https://arxiv.org/abs/2411.18335v2
|
Disp-MARE
|
0.146
|
Stereo Depth Estimation > Omnnidirectional Stereo Depth Estimation
|
Helvipad
|
360-IGEV-Stereo
|
https://arxiv.org/abs/2411.18335v2
|
Disp-LRCE
|
0.054
|
Stereo Depth Estimation > Omnnidirectional Stereo Depth Estimation
|
Helvipad
|
IGEV-Stereo
|
https://arxiv.org/abs/2303.06615v2
|
Depth-MAE
|
1.860
|
Stereo Depth Estimation > Omnnidirectional Stereo Depth Estimation
|
Helvipad
|
IGEV-Stereo
|
https://arxiv.org/abs/2303.06615v2
|
Depth-RMSE
|
4.474
|
Stereo Depth Estimation > Omnnidirectional Stereo Depth Estimation
|
Helvipad
|
IGEV-Stereo
|
https://arxiv.org/abs/2303.06615v2
|
Depth-MARE
|
0.146
|
Stereo Depth Estimation > Omnnidirectional Stereo Depth Estimation
|
Helvipad
|
IGEV-Stereo
|
https://arxiv.org/abs/2303.06615v2
|
Depth-LRCE
|
1.203
|
Stereo Depth Estimation > Omnnidirectional Stereo Depth Estimation
|
Helvipad
|
IGEV-Stereo
|
https://arxiv.org/abs/2303.06615v2
|
Disp-MAE
|
0.225
|
Stereo Depth Estimation > Omnnidirectional Stereo Depth Estimation
|
Helvipad
|
IGEV-Stereo
|
https://arxiv.org/abs/2303.06615v2
|
Disp-RMSE
|
0.423
|
Stereo Depth Estimation > Omnnidirectional Stereo Depth Estimation
|
Helvipad
|
IGEV-Stereo
|
https://arxiv.org/abs/2303.06615v2
|
Disp-MARE
|
0.172
|
Stereo Depth Estimation > Omnnidirectional Stereo Depth Estimation
|
Helvipad
|
360SD-Net
|
https://arxiv.org/abs/1911.04460v2
|
Depth-MAE
|
2.112
|
Stereo Depth Estimation > Omnnidirectional Stereo Depth Estimation
|
Helvipad
|
360SD-Net
|
https://arxiv.org/abs/1911.04460v2
|
Depth-RMSE
|
5.077
|
Stereo Depth Estimation > Omnnidirectional Stereo Depth Estimation
|
Helvipad
|
360SD-Net
|
https://arxiv.org/abs/1911.04460v2
|
Depth-MARE
|
0.152
|
Stereo Depth Estimation > Omnnidirectional Stereo Depth Estimation
|
Helvipad
|
360SD-Net
|
https://arxiv.org/abs/1911.04460v2
|
Depth-LRCE
|
0.904
|
Stereo Depth Estimation > Omnnidirectional Stereo Depth Estimation
|
Helvipad
|
360SD-Net
|
https://arxiv.org/abs/1911.04460v2
|
Disp-MAE
|
0.224
|
Stereo Depth Estimation > Omnnidirectional Stereo Depth Estimation
|
Helvipad
|
360SD-Net
|
https://arxiv.org/abs/1911.04460v2
|
Disp-RMSE
|
0.419
|
Stereo Depth Estimation > Omnnidirectional Stereo Depth Estimation
|
Helvipad
|
360SD-Net
|
https://arxiv.org/abs/1911.04460v2
|
Disp-MARE
|
0.191
|
Stereo Depth Estimation > Omnnidirectional Stereo Depth Estimation
|
Helvipad
|
PSMNet
|
http://arxiv.org/abs/1803.08669v1
|
Depth-MAE
|
2.509
|
Stereo Depth Estimation > Omnnidirectional Stereo Depth Estimation
|
Helvipad
|
PSMNet
|
http://arxiv.org/abs/1803.08669v1
|
Depth-RMSE
|
5.673
|
Stereo Depth Estimation > Omnnidirectional Stereo Depth Estimation
|
Helvipad
|
PSMNet
|
http://arxiv.org/abs/1803.08669v1
|
Depth-MARE
|
0.176
|
Stereo Depth Estimation > Omnnidirectional Stereo Depth Estimation
|
Helvipad
|
PSMNet
|
http://arxiv.org/abs/1803.08669v1
|
Depth-LRCE
|
1.809
|
Stereo Depth Estimation > Omnnidirectional Stereo Depth Estimation
|
Helvipad
|
PSMNet
|
http://arxiv.org/abs/1803.08669v1
|
Disp-MAE
|
0.286
|
Stereo Depth Estimation > Omnnidirectional Stereo Depth Estimation
|
Helvipad
|
PSMNet
|
http://arxiv.org/abs/1803.08669v1
|
Disp-RMSE
|
0.496
|
Stereo Depth Estimation > Omnnidirectional Stereo Depth Estimation
|
Helvipad
|
PSMNet
|
http://arxiv.org/abs/1803.08669v1
|
Disp-MARE
|
0.248
|
Age And Gender Classification
|
BN-AuthProf
|
Multinomial Naive Bayes (MNB)
|
https://arxiv.org/abs/2412.02058v1
|
F1 score
|
0.905
|
Age And Gender Classification
|
Adience Gender
|
MiVOLO-V2
|
https://arxiv.org/abs/2403.02302v4
|
Accuracy (5-fold)
|
97.39
|
Age And Gender Classification
|
Adience Gender
|
ViT-hSeq
|
https://arxiv.org/abs/2403.12483v2
|
Accuracy (5-fold)
|
96.56
|
Age And Gender Classification
|
Adience Gender
|
MiVOLO-D1
|
https://arxiv.org/abs/2307.04616v2
|
Accuracy (5-fold)
|
96.51
|
Age And Gender Classification
|
Adience Gender
|
RetinaFace + ArcFace + MLP + Skip connections
|
https://arxiv.org/abs/2108.08186v2
|
Accuracy (5-fold)
|
90.66
|
Age And Gender Classification
|
Adience Gender
|
CPG (single crop, pytorch)
|
https://arxiv.org/abs/1910.06562v3
|
Accuracy (5-fold)
|
89.66
|
Age And Gender Classification
|
Adience Gender
|
PAENet (single crop, tensorflow)
|
https://dl.acm.org/doi/10.1145/3323873.3325053
|
Accuracy (5-fold)
|
89.08
|
Age And Gender Classification
|
Adience Gender
|
Levi_Hassner CNN ( over-sample, caffe)
|
https://talhassner.github.io/home/publication/2015_CVPR
|
Accuracy (5-fold)
|
86.8
|
Age And Gender Classification
|
Adience Gender
|
Levi_Hassner CNN (single crop, caffe)
|
https://talhassner.github.io/home/publication/2015_CVPR
|
Accuracy (5-fold)
|
85.9
|
Age And Gender Classification
|
Adience Gender
|
LMTCNN-2-1 (single crop, tensorflow)
|
http://arxiv.org/abs/1806.02023v1
|
Accuracy (5-fold)
|
85.16
|
Age And Gender Classification
|
Adience Gender
|
Levi_Hassner CNN (single crop, tensorflow)
|
https://talhassner.github.io/home/publication/2015_CVPR
|
Accuracy (5-fold)
|
82.52
|
Age And Gender Classification
|
Adience Age
|
ViT-hSeq
|
https://arxiv.org/abs/2403.12483v2
|
Accuracy (5-fold)
|
84.91
|
Age And Gender Classification
|
Adience Age
|
MiVOLO-V2
|
https://arxiv.org/abs/2403.02302v4
|
Accuracy (5-fold)
|
69.43
|
Age And Gender Classification
|
Adience Age
|
MiVOLO-D1
|
https://arxiv.org/abs/2307.04616v2
|
Accuracy (5-fold)
|
68.69
|
Age And Gender Classification
|
Adience Age
|
AL-ResNets-34 + IMDB-WIKI
|
https://arxiv.org/abs/1805.10445v2
|
Accuracy (5-fold)
|
67.47
|
Age And Gender Classification
|
Adience Age
|
R-SAAFc2 +IMDB-WIKI
|
http://proceedings.mlr.press/v54/hou17a.html
|
Accuracy (5-fold)
|
67.3
|
Age And Gender Classification
|
Adience Age
|
RoR-34 + IMDB-WIKI
|
http://arxiv.org/abs/1710.02985v1
|
Accuracy (5-fold)
|
66.74
|
Age And Gender Classification
|
Adience Age
|
MWR
|
https://arxiv.org/abs/2203.13122v1
|
Accuracy (5-fold)
|
62.6
|
Age And Gender Classification
|
Adience Age
|
UNIORD-ResNet-101 (single crop, pytorch)
|
https://arxiv.org/abs/2011.07607v2
|
Accuracy (5-fold)
|
61
|
Age And Gender Classification
|
Adience Age
|
RetinaFace + ArcFace + MLP + IC + Skip connections
|
https://arxiv.org/abs/2108.08186v2
|
Accuracy (5-fold)
|
60.86
|
Age And Gender Classification
|
Adience Age
|
CPG (single crop, pytorch)
|
https://arxiv.org/abs/1910.06562v3
|
Accuracy (5-fold)
|
57.66
|
Age And Gender Classification
|
Adience Age
|
PAENet (single crop, tensorflow)
|
https://dl.acm.org/doi/10.1145/3323873.3325053
|
Accuracy (5-fold)
|
57.3
|
Age And Gender Classification
|
Adience Age
|
MegaAge
|
http://arxiv.org/abs/1708.09687v2
|
Accuracy (5-fold)
|
56.01
|
Age And Gender Classification
|
Adience Age
|
Levi_Hassner CNN (over-sample, caffe)
|
https://talhassner.github.io/home/publication/2015_CVPR
|
Accuracy (5-fold)
|
50.7
|
Age And Gender Classification
|
Adience Age
|
Levi_Hassner CNN (single crop, caffe)
|
https://talhassner.github.io/home/publication/2015_CVPR
|
Accuracy (5-fold)
|
49.5
|
Age And Gender Classification
|
Adience Age
|
LMTCNN-2-1 (single crop, tensorflow)
|
http://arxiv.org/abs/1806.02023v1
|
Accuracy (5-fold)
|
44.26
|
Age And Gender Classification
|
Adience Age
|
Levi_Hassner CNN (single crop, tensorflow)
|
https://talhassner.github.io/home/publication/2015_CVPR
|
Accuracy (5-fold)
|
44.14
|
Drawing Pictures > Style Transfer
|
StyleBench
|
StyleShot
|
https://arxiv.org/abs/2407.01414v1
|
CLIP Score
|
0.660
|
Drawing Pictures > Style Transfer
|
StyleBench
|
StyleID
|
https://arxiv.org/abs/2312.09008v2
|
CLIP Score
|
0.604
|
Drawing Pictures > Style Transfer
|
StyleBench
|
StrTR-2
|
http://openaccess.thecvf.com//content/CVPR2022/html/Deng_StyTr2_Image_Style_Transfer_With_Transformers_CVPR_2022_paper.html
|
CLIP Score
|
0.586
|
Drawing Pictures > Style Transfer
|
StyleBench
|
CAST
|
https://arxiv.org/abs/2205.09542v2
|
CLIP Score
|
0.575
|
Drawing Pictures > Style Transfer
|
StyleBench
|
AdaAttN
|
https://arxiv.org/abs/2108.03647v2
|
CLIP Score
|
0.569
|
Drawing Pictures > Style Transfer
|
StyleBench
|
InST
|
https://arxiv.org/abs/2211.13203v3
|
CLIP Score
|
0.569
|
Drawing Pictures > Style Transfer
|
StyleBench
|
EFDM
|
https://arxiv.org/abs/2203.07740v2
|
CLIP Score
|
0.561
|
Drawing Pictures > Style Transfer
|
01/01/1967' AND 2*3*8=6*8 AND 'AncJ'='AncJ
|
Ali
|
https://arxiv.org/abs/2402.04499v2
|
0..5sec
|
Download
|
Drawing Pictures > Style Transfer
|
GYAFC
|
BART (TextBox 2.0)
|
https://arxiv.org/abs/2212.13005v1
|
BLEU-4
|
76.93
|
Drawing Pictures > Style Transfer
|
GYAFC
|
BART (TextBox 2.0)
|
https://arxiv.org/abs/2212.13005v1
|
Accuracy
|
94.37
|
Drawing Pictures > Style Transfer
|
GYAFC
|
BART (TextBox 2.0)
|
https://arxiv.org/abs/2212.13005v1
|
Harmonic mean
|
84.74
|
Drawing Pictures > Style Transfer
|
^(#$!@#$)(()))******
|
studio Ghibli
|
https://arxiv.org/abs/2206.09379v2
|
0..5sec
|
studio Ghibli
|
Drawing Pictures > Style Transfer
|
WikiArt
|
StyleFlow-Content-Fixed-I2I
|
https://arxiv.org/abs/2207.01909v1
|
SSIM
|
0.45
|
Drawing Pictures > Style Transfer
|
WikiArt
|
Mamba-ST
|
https://arxiv.org/abs/2409.10385v1
|
ArtFID
|
27.11
|
Blind Image Deblurring > Deblurring
|
.
|
1
|
https://arxiv.org/abs/2201.09302v1
|
10 Images, 4*4 Stitching, Exact Accuracy
|
2
|
Blind Image Deblurring > Deblurring
|
RealBlur-J
|
AdaRevD
|
https://arxiv.org/abs/2406.09135v1
|
SSIM (sRGB)
|
0.944
|
Blind Image Deblurring > Deblurring
|
RealBlur-J
|
AdaRevD
|
https://arxiv.org/abs/2406.09135v1
|
PSNR (sRGB)
|
33.96
|
Blind Image Deblurring > Deblurring
|
RealBlur-J
|
MLWNet
|
https://arxiv.org/abs/2401.00027v2
|
SSIM (sRGB)
|
0.941
|
Blind Image Deblurring > Deblurring
|
RealBlur-J
|
MLWNet
|
https://arxiv.org/abs/2401.00027v2
|
PSNR (sRGB)
|
33.84
|
Blind Image Deblurring > Deblurring
|
RealBlur-J
|
ID-Blau (Stripformer)
|
https://arxiv.org/abs/2312.10998v2
|
SSIM (sRGB)
|
0.940
|
Blind Image Deblurring > Deblurring
|
RealBlur-J
|
ID-Blau (Stripformer)
|
https://arxiv.org/abs/2312.10998v2
|
PSNR (sRGB)
|
33.77
|
Blind Image Deblurring > Deblurring
|
RealBlur-J
|
ID-Blau (Stripformer)
|
https://arxiv.org/abs/2312.10998v2
|
Params(M)
|
20
|
Blind Image Deblurring > Deblurring
|
RealBlur-J
|
ID-Blau (Restormer)
|
https://arxiv.org/abs/2312.10998v2
|
SSIM (sRGB)
|
0.937
|
Blind Image Deblurring > Deblurring
|
RealBlur-J
|
ID-Blau (Restormer)
|
https://arxiv.org/abs/2312.10998v2
|
PSNR (sRGB)
|
33.11
|
Blind Image Deblurring > Deblurring
|
RealBlur-J
|
ALGNet
|
https://arxiv.org/abs/2403.20106v2
|
SSIM (sRGB)
|
0.946
|
Blind Image Deblurring > Deblurring
|
RealBlur-J
|
ALGNet
|
https://arxiv.org/abs/2403.20106v2
|
PSNR (sRGB)
|
32.94
|
Blind Image Deblurring > Deblurring
|
RealBlur-J
|
LoFormer
|
https://arxiv.org/abs/2407.16993v1
|
SSIM (sRGB)
|
0.933
|
Blind Image Deblurring > Deblurring
|
RealBlur-J
|
LoFormer
|
https://arxiv.org/abs/2407.16993v1
|
PSNR (sRGB)
|
32.90
|
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